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After over half a century of rapid development, the geometrical scaling of transistors predicted by Moore’s law has approached the quantum physics limits, and the information society is entering the post-Moore era. Cutting-edge research on relevant fields has already been carried out, and new concepts such as beyond-Moore devices, in-sensor computing, and neuromorphic computing have emerged one after another. The number of new materials, devices, and technologies are all growing, and their broad prospects are demonstrated in emerging areas including big data, internet of things (IoT), and artificial intelligence (AI). This article reviews the development progress of core post-Moore device technologies and the challenges they confront, summarizes the future development trends and application prospects, and put forward some suggestions accordingly.
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在经过半个多世纪的高速发展后,摩尔定律预测的晶体管尺寸缩微已接近量子物理极限,信息社会正在进入后摩尔时代。相关领域的前沿研究早已展开,超摩尔器件、感存算一体、神经形态计算等新概念纷纷涌现,各种新材料、新器件和新技术层出不穷,并在大数据、物联网、人工智能等新兴领域展现出广阔的前景。文章重点梳理了核心后摩尔器件技术的发展脉络,分析其面临的挑战,总结未来发展趋势和应用前景,并提出相应建议。
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王佩剑,研究员,博士研究生导师。浙江大学杭州国际科创中心“求是科创学者”,杭州市级领军人才。研究方向为低维材料生长制备、物性表征/调控、集成与器件应用。近3年来在Nature Communications、ACS Nano、Nano Letters、Applied Physics Reviews、Advanced Materials、Chemical Science等期刊发表论文17篇,研究团队发表论文40余篇。主持国家自然科学基金青年科学基金1项。受邀为Journal of Magnetism and Magnetic Materials、Nanoscale、Journal of Physical Chemistry C等期刊审稿。获美国物理学会会议“Best Poster Award”奖和UMass专业发展旅行奖等。获授权发明专利3项。电子信箱: pjwang@zju.edu.cn。 |
 |
俞滨,教授,博士研究生导师。浙江大学杭州国际科创中心战略科学家、未来科学研究院院长。美国国家发明家科学院(NAI)院士,电气与电子工程师协会(IEEE)会士,斯坦福大学顾问教授,北京大学客座教授等。研究方向为类脑感知与计算、后摩尔电子学、微纳集成芯片。在硅基/碳基/低维半导体电子学、新型逻辑/存储器件、纳米传感等领域做出多项成果,在Nature Electronics、Nature Communications、Advanced Materials、ACS Nano和IEEE旗舰期刊,IEDM、VLSI Symposium等顶级国际会议发表论文300余篇。受聘于国际半导体技术路线图(ITRS)起草委员会、IEEE EDS和IEEE NTC执行委员会、IEEE Fellow评审委员会、IEEE George Smith奖评审委员会。担任IEEE EDL、IEEE T-NANO、IEEE T-ED、Nano-Micro Letters等学术期刊编辑/特邀编辑。获IEEE杰出讲座奖和IBM学者奖。电子信箱: yu-bin@zju.edu.cn。 |
, authorsList=王佩剑, 周嘉超, 胡加杨, 李涵茜, 田丰, 张致翔, 柴健, 徐心艺, 卞正, 赵昱达, 徐杨, 俞滨)}, authors=[Author(id=1241718684511694868, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=pjwang@zju.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1241718684608163864, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, authorId=1241718684511694868, language=EN, stringName=Peijian WANG, firstName=Peijian, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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王佩剑,研究员,博士研究生导师。浙江大学杭州国际科创中心“求是科创学者”,杭州市级领军人才。研究方向为低维材料生长制备、物性表征/调控、集成与器件应用。近3年来在Nature Communications、ACS Nano、Nano Letters、Applied Physics Reviews、Advanced Materials、Chemical Science等期刊发表论文17篇,研究团队发表论文40余篇。主持国家自然科学基金青年科学基金1项。受邀为Journal of Magnetism and Magnetic Materials、Nanoscale、Journal of Physical Chemistry C等期刊审稿。获美国物理学会会议“Best Poster Award”奖和UMass专业发展旅行奖等。获授权发明专利3项。电子信箱: pjwang@zju.edu.cn。
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王佩剑,研究员,博士研究生导师。浙江大学杭州国际科创中心“求是科创学者”,杭州市级领军人才。研究方向为低维材料生长制备、物性表征/调控、集成与器件应用。近3年来在Nature Communications、ACS Nano、Nano Letters、Applied Physics Reviews、Advanced Materials、Chemical Science等期刊发表论文17篇,研究团队发表论文40余篇。主持国家自然科学基金青年科学基金1项。受邀为Journal of Magnetism and Magnetic Materials、Nanoscale、Journal of Physical Chemistry C等期刊审稿。获美国物理学会会议“Best Poster Award”奖和UMass专业发展旅行奖等。获授权发明专利3项。电子信箱: pjwang@zju.edu.cn。
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俞滨,教授,博士研究生导师。浙江大学杭州国际科创中心战略科学家、未来科学研究院院长。美国国家发明家科学院(NAI)院士,电气与电子工程师协会(IEEE)会士,斯坦福大学顾问教授,北京大学客座教授等。研究方向为类脑感知与计算、后摩尔电子学、微纳集成芯片。在硅基/碳基/低维半导体电子学、新型逻辑/存储器件、纳米传感等领域做出多项成果,在Nature Electronics、Nature Communications、Advanced Materials、ACS Nano和IEEE旗舰期刊,IEDM、VLSI Symposium等顶级国际会议发表论文300余篇。受聘于国际半导体技术路线图(ITRS)起草委员会、IEEE EDS和IEEE NTC执行委员会、IEEE Fellow评审委员会、IEEE George Smith奖评审委员会。担任IEEE EDL、IEEE T-NANO、IEEE T-ED、Nano-Micro Letters等学术期刊编辑/特邀编辑。获IEEE杰出讲座奖和IBM学者奖。电子信箱: yu-bin@zju.edu.cn。
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俞滨,教授,博士研究生导师。浙江大学杭州国际科创中心战略科学家、未来科学研究院院长。美国国家发明家科学院(NAI)院士,电气与电子工程师协会(IEEE)会士,斯坦福大学顾问教授,北京大学客座教授等。研究方向为类脑感知与计算、后摩尔电子学、微纳集成芯片。在硅基/碳基/低维半导体电子学、新型逻辑/存储器件、纳米传感等领域做出多项成果,在Nature Electronics、Nature Communications、Advanced Materials、ACS Nano和IEEE旗舰期刊,IEDM、VLSI Symposium等顶级国际会议发表论文300余篇。受聘于国际半导体技术路线图(ITRS)起草委员会、IEEE EDS和IEEE NTC执行委员会、IEEE Fellow评审委员会、IEEE George Smith奖评审委员会。担任IEEE EDL、IEEE T-NANO、IEEE T-ED、Nano-Micro Letters等学术期刊编辑/特邀编辑。获IEEE杰出讲座奖和IBM学者奖。电子信箱: yu-bin@zju.edu.cn。
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Nano Letters,
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ACS Applied Materials & Interfaces,
2019,
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Nano Letters,
2019,
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2018, doi:
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Nature Electronics,
2020,
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Advanced Functional Materials,
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Nature Nanotechnology,
2020,
15(9): 776-782., articleTitle=Power-efficient neural network with artificial dendrites, refAbstract=In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic signals. The lack of these critical functions in artificial neural networks compromises their performance, for example in terms of flexibility, energy efficiency and the ability to handle complex tasks. Here, by developing artificial dendrites, we experimentally demonstrate a complete neural network fully integrated with synapses, dendrites and soma, implemented using scalable memristor devices. We perform a digit recognition task and simulate a multilayer network using experimentally derived device characteristics. The power consumption is more than three orders of magnitude lower than that of a central processing unit and 70 times lower than that of a typical application-specific integrated circuit chip. This network, equipped with functional dendrites, shows the potential of substantial overall performance improvement, for example by extracting critical information from a noisy background with significantly reduced power consumption and enhanced accuracy.)], funds=[Fund(id=1241718691700732033, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, awardId=51902061, language=CN, fundingSource=国家自然科学基金青年科学基金(51902061), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241718683010134028, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, xref=null, ext=[AuthorCompanyExt(id=1241718683018522637, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, companyId=1241718683010134028, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. School of Micro-Nano Electronics, Zhejiang University, Hangzhou 311200, China), AuthorCompanyExt(id=1241718683026911246, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, companyId=1241718683010134028, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.浙江大学微纳电子学院,杭州 311200)]), AuthorCompany(id=1241718684369088527, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, xref=null, ext=[AuthorCompanyExt(id=1241718684394254352, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, companyId=1241718684369088527, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China), AuthorCompanyExt(id=1241718684436197394, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, companyId=1241718684369088527, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.浙江大学杭州国际科创中心,杭州 311215)])], figs=[ArticleFig(id=1241718689846849640, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, language=EN, label=null, caption=null, figureFileSmall=+nuD+Nxz/5ijQVxeFFsr0g==, figureFileBig=rpqp/sXg330NGEjWe3jqCQ==, tableContent=null), ArticleFig(id=1241718689913958505, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1241718638936380083, language=CN, label=图1, caption=
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2012—2021年与忆阻器相关的文献数量(a)和不同学科主题中带有忆阻器关键词的文献数量(b)
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